yegappan

Vim Script for Python Developers

This is a guide to Vim Script development for Python developers. Sample code for the various expressions, statements, functions and programming constructs is shown in both Python and Vim Script. This is not intended to be a tutorial for developing Vim scripts. It is assumed that the reader is familiar with Python programming.

For an introduction to Vim Script development, refer to usr_41.txt, eval.txt and Learn Vimscript the Hard Way

For a guide similar to this one for JavaScript developers, refer to Vim Script for the JavaScripter

This guide only describes the programming constructs that are present in both Python and Vim. The constructs that are unique to Vim (e.g. autocommands, [key-mapping](https://vimhelp.org/map.txt.html#key-m

noamross

-- a lua filter for panodoc
-- run pandoc your_word_doc.docx --track-change=all -t markdown --lua-filter=criticmarkup.lua
-- TODO: Detect substitutions in adjacent insertion/deletions
-- TODO: capture whole comment hightlight rather than just start point of comment
function Span(elem)
  if elem.classes[1] and elem.classes[1] == "insertion" then
    local opener = { pandoc.RawInline(FORMAT, "{++ ") }
    local closer = { pandoc.RawInline(FORMAT, " ++}") }
    return opener .. elem.content .. closer
  elseif

MaximeWack

#! /usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import print_function

help_text = """
Extracts tags from rmd files. Useful for the Tagbar plugin.

Usage:
Install Tagbar (http://majutsushi.github.io/tagbar/). Then, put this file

Pakillo

library(sf)
library(ggspatial)
library(ggthemes)

coast <- rnaturalearth::ne_coastline(scale = "small", returnclass = "sp")

forests <- st_read("data/forestmap/cfc_gen.shp")
forest.redux <- rmapshaper::ms_simplify(forests)
forest.redux.sp <- as(forest.redux, "Spatial")

jeroenjanssens

library(tidyverse)
library(magrittr)
library(stringr)
library(readxl)

directory <- "bunch/of/excel/files"

# Get an overview of all the Excel files and their sheets
sheets <-
  data_frame(file = list.files(directory, full.names = TRUE),

paulrougieux

#' Extract link texts and urls from a web page
#' @param url character an url
#' @return a data frame of link text and urls
#' @examples
#' \dontrun{
#' scraplinks("http://localhost/")
#' glinks <- scraplinks("http://google.com/")
#' }
#' @export
scraplinks <- function(url){

sdtaylor

prior_version = 3.3

library_path = .libPaths()[1]
library_path = substr(library_path, 1, nchar(library_path)-3)

old_library_path=paste0(library_path, prior_version)

old_packages = list.files(old_library_path)

install.packages(old_packages)

arthurattwell

Useful regex during manuscript cleanup

• Copy-paste to split book into separate chapter files (also see split.sh)
• Add non-breaking space in range of numbers
• Fix inline spans broken by a space after a word before the closing *
• Remove image width and height inherited from images in docx
• Wrap all images in an Electric Book figure blockquote
• Simplify indentation in lists by reducing space after list marker to one space
• [Remove non-kramdown markdown ^ around superscripts after numbers](#remove-non-kramdown-markdown--around-superscripts-a

conormm

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

jslefche

The Price Equation for Partitioning Diversity Effects on Ecosystem Function

This function takes a data.frame corresponding to the site-by-species "functioning" matrix (where cells contain the values of the ecosystem function), and returns the five additive components of the Price equation.

EXAMPLE

# Example 1: all species contribute equally to functioning and all occur at the baseline site
# Here, RICH_L should be negative and equal the total number of unshared species at each site
# COMP and CDE terms should be zero